## Source: local data table [475,719 x 102]
## Call: `_DT1`[!`_DT2`, on = .(word)]
##
## FACILITY_CITY ACTIVI…¹ OWNER…² OWNER…³ FACIL…⁴ RECOR…⁵ PROGR…⁶ PROGR…⁷ PROGR…⁸
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 ALHAMBRA 2021/10… OW0269… SKATE … FA0280… PR0235… SKATES… ACTIVE 1634
## 2 ALHAMBRA 2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE 1638
## 3 ALHAMBRA 2018/05… OW0031… SANCHE… FA0006… PR0037… BUN N … ACTIVE 1638
## 4 ALHAMBRA 2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE 1633
## 5 ALHAMBRA 2021/07… OW0033… STARBU… FA0048… PR0005… STARBU… ACTIVE 1633
## 6 ALHAMBRA 2019/05… OW0185… SAN TU… FA0179… PR0173… RICK'S… ACTIVE 1638
## # … with 475,713 more rows, 93 more variables: PE_DESCRIPTION <chr>,
## # FACILITY_ADDRESS <chr>, FACILITY_STATE <chr>, FACILITY_ZIP <int>,
## # SERVICE_CODE <int>, SERVICE_DESCRIPTION <chr>, SCORE <int>,
## # SERIAL_NUMBER <chr>, EMPLOYEE_ID <chr>, ObjectId.x <int>, Pop_Tot <int>,
## # Prop_18y <dbl>, Prop_64y <dbl>, Prop_65y_ <dbl>, Prop_Blk <dbl>,
## # Prop_Lat <dbl>, Prop_Whi <dbl>, Prop_Asi <dbl>, Prop_Ami <dbl>,
## # Prop_NHO <dbl>, Prop_FPL1 <dbl>, Prop_FPL2 <dbl>, Prop_forb <dbl>, …
##
## # Use as.data.table()/as.data.frame()/as_tibble() to access results
Showcasing plots
Restaurant Inspection Score vs Diabetes
Restaurant Inspection Score vs Obesity
scatter2
Restaurant Inspection Score vs Depression
scatter3
Map of chain restaurants with heat map of proportion with
diabetes
DMmap